Accurate channel state information estimation is crucial for reliable wireless networks [1]}, [2]}, [3]}, [4]}.
Conventional least square (LS) based channel estimation is widely used in long-term evolution (LTE) and WiFi networks due to its low complexity and latency. However, it performs poorly at a low signal-to-noise ratio (SNR) [5]}. Other techniques like linear minimum mean square error (LMMSE) offer better performance than LS but need prior knowledge of noise, and second-order channel statistics [6]}. Furthermore, it is computationally intensive [6]}, [8]}.
The limited sub-6 GHz spectrum has led to the co-existence of heterogeneous devices with limited guard bands. This has resulted in poor SNR due to high noise floor and adjacent-channel interference. This has led to significant interest in improving the performance of the conventional LS and LMMSE channel estimation approaches [9]} [10]}. With the integration of vehicular and wireless networks, the radio environment has become increasingly dynamic, posing serious challenges in establishing reliable communication links. Furthermore, high-speed ultra-reliable services demand ultra-low latency of the order of milliseconds [11]}.
